Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43963862" target="_blank" >RIV/49777513:23220/21:43963862 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9666639" target="_blank" >https://ieeexplore.ieee.org/document/9666639</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/UEMCON53757.2021.9666639" target="_blank" >10.1109/UEMCON53757.2021.9666639</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers

Popis výsledku v původním jazyce

Designing power amplifiers based on the demanded power and frequency is one of the challenging processes of circuits design in electrical engineering. This is best understood when it comes to thermal noises and other unwanted agents. This is why the application of cloud-based methods can be beneficial to save time and money for designing such complex systems. In this paper, several machine learning (ML) approaches have been used to design a class E amplifier. In this regard, the proposed methods, which are implemented via Microsoft Azure, are used to model and predict the circuit element values of the class E amplifier. In order to reach a reliable design, some important unwanted factors such as nonlinear parasitic elements of the transistor are considered. The results demonstrated that not only can the proposed could-based techniques estimate such elements accurately, but also working with such tools are really easy.

Název v anglickém jazyce

Cloud-based machine learning techniques implemented by microsoft azure for designing power amplifiers

Popis výsledku anglicky

Designing power amplifiers based on the demanded power and frequency is one of the challenging processes of circuits design in electrical engineering. This is best understood when it comes to thermal noises and other unwanted agents. This is why the application of cloud-based methods can be beneficial to save time and money for designing such complex systems. In this paper, several machine learning (ML) approaches have been used to design a class E amplifier. In this regard, the proposed methods, which are implemented via Microsoft Azure, are used to model and predict the circuit element values of the class E amplifier. In order to reach a reliable design, some important unwanted factors such as nonlinear parasitic elements of the transistor are considered. The results demonstrated that not only can the proposed could-based techniques estimate such elements accurately, but also working with such tools are really easy.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Elektrotechnické technologie s vysokým podílem vestavěné inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2021 IEEE 12th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (IEEE UEMCON)

ISBN

978-1-66540-690-1

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

0041-0044

Název nakladatele

IEEE

Místo vydání

Piscaway

Místo konání akce

virtual, New York, USA

Datum konání akce

1. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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